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Intro to Spark Data Science

March 18 - March 19

The popular “Intro to Spark for Data Science” weekend workshop is back!

Register now with the code “SEASPARK50” to get 50% off!

After completing this weekend workshop, you’ll be better prepared to use Apache Spark for real projects and problems on your own. We’ll use Spark to power product recommendations and natural language processing tasks. In just 2 days, you will take your data skills to the next level by using Spark to build data pipelines.

Workshop instructors will be on-hand all weekend to teach, live code, and help debug as you work through the course materials.

Who is this course for?

This is an introductory course on Spark for anyone who has a personal or professional interest in data science.

We don’t assume you know anything in particular about Spark. All you need to come to our workshop is a working knowledge of programming to get through the course materials, a Macintosh or Linux laptop, and a readiness to learn.

If you don’t have a Mac or Linux computer, we can provide a Galvanize workstation for your use during the workshop.

The more you know before the course, the more you’ll get out of it, so we do recommend the pre-course materials below:

  • We strongly recommend installing Anaconda, the distribution of Python that is most useful for data science and data engineering.
  • Learn Python The Hard Way is a free, long-form Python tutorial that will help to reinforce your basic Python skills, especially if you are relatively new to Python coding.
  • After you register for the course, your Galvanize instructors will provide detailed instructions for installing and configuring Spark on your computer, and will help you along the way if you need it.

Course outline

March 18 & 19, 2017, 9:00 AM – 5:00 PM (Lunch & snacks provided)

In this two-day, in-person, hands-on Spark course, we will:

  • Import, clean, and query data using Spark RDDs and Spark SQL.
  • Build a product recommendation system using Spark.
  • Perform Natural Language Processing (NLP) using Spark.
  • Use Amazon Web Services (AWS) to deploy a Spark cluster.
  • Use a Spark cluster to process large datasets that cannot fit on your personal computer.

Meet Your Instructors

Jean-François (Jeff) Omhover, Galvanize Data Science Instructor

Jeff is a Senior Data Scientist and Instructor in the Galvanize Data Science Immersive program. Prior to joining Galvanize, Jeff was an Assistant Professor at one of the leading engineering schools in France. He managed large-scale multidisciplinary research projects in partnership between industry and academia. He has used Spark and Natural Language Processing for mining consumer sentiment and brand perception from user comments, and for mining concepts from scientific papers.

Miles Erickson, Galvanize Data Science Associate Instructor

Miles is a Data Scientist and Associate Instructor in the Galvanize Data Science Immersive program. Before joining Galvanize, Miles worked as a systems/network engineering consultant and taught college-level classes in IT infrastructure and security. Miles has contributed to the development of widely recognized certification exams for server engineers. Miles is a graduate of the University of Washington and is a co-organizer of the local Python community in Seattle.

Why Spark?

Spark is a powerful, open-source processing engine for data distributed across large clusters. Spark is optimized for speed and ease of use; it uses caching and memory to run distributed algorithms 100x faster than MapReduce. Spark can be used for batch processing and for processing data in near real-time.

Also, used by data professionals at Amazon, eBay, NASA, and 200+ other organizations, Spark’s community is one of the fastest growing in the world.

Software & Prerequisite Requirements

This workshop series is for anyone who wants to use Spark to analyze data at scale.

Programming Experience:

Course examples and exercises will use Python and PySpark. Basic working knowledge of the Python programming language (i.e. the ability to write scripts and functions in Python) is required.

Command Line & Version Control:

Basic knowledge of Unix commands (i.e. command line) is required.
We will use GitHub for sharing and maintaining code. If you do not already have a GitHub account, you will need to create one before class begins.

Technology Requirements:

Students are expected to bring a personal computer running the Mac OS X or Linux operating system, with at least 4GB of RAM and at least 6GB of free disk space.


Galvanize – Pioneer Square Seattle
111 S Jackson Street
Seattle, WA 98104 US
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